Search results for: plant disease classification

Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: data envelopment analysis, interval DEA, efficiency classification, efficiency prediction

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Authors: Kambiz Hassanzadeh, Seyedeh Shohreh Ebrahimi, Shahrbanoo Oryan, Arman Rahimmi, Esmael Izadpanah

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Parkinson’s disease is one of the most prevalent neurodegenerative diseases which occurs in elderly. There are convincing evidences that oxidative stress has an important role in both the initiation and progression of Parkinson’s disease. Thymoquinone (TQ) is shown to have antioxidant and anti-inflammatory properties in invitro and invivo studies. It is well documented that TQ acts as a free radical scavenger and prevents the cell damage. Therefore this study aimed to evaluate the effect of TQ on motor and non-motor symptoms in animal model of Parkinson’s disease. Male Wistar rats (10-12 months) received rotenone (1mg/kg/day, sc) to induce Parkinson’s disease model. Pretreatment with TQ (7.5 and 15 mg/kg/day, po) was administered one hour before the rotenone injection. Three motor tests (rotarod, rearing and bar tests) and two non-motor tests (forced swimming and elevated plus maze) were performed for behavioral assessment. Our results indicated that TQ significantly ameliorated the rotenone-induced motor dysfunction in rotarod and rearing tests also it could prevent the non-motor dysfunctions in forced swimming and elevated plus maze tests. In conclusion we found that TQ delayed the Parkinson's disease induction by rotenone and this effect might be related to its proved antioxidant effect.

Keywords: Parkinson's disease, thymoquinone, motor and non-motor symptoms, neurodegenerative disease

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

Procedia PDF Downloads 320

Authors: Jeremiah L. Chukwuneke, Obiora E. Anisiji, Chinonso H. Achebe, Paul C. Okolie

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This paper developed a mathematical model of a commercial biogas plant for urban area clean energy requirement. It identified biodegradable waste materials like domestic/city refuse as economically viable alternative source of energy. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analyses were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500 m3 power gas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of bio gas production is essentially a function of the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.

Keywords: energy and mass conservation, specific growth rate, thermophilic bacteria, temperature, rate of bio gas production

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: P. Kanakasabapathy, S. Radhika

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In the competitive electricity market environment, the profit of the pumped-storage plant in the energy market can be maximized by operating it as a generator, when market clearing price is high and as a pump, to pump water from lower reservoir to upper reservoir, when the price is low. An optimal self-scheduling plan has been developed for a pumped-storage plant, carried out on weekly basis in order to maximize the profit of the plant, keeping into account of all the major uncertainties such as the sudden ancillary service delivery request and the price forecasting errors. For a pumped storage power plant to operate in a real time market successive self-scheduling has to be done by considering the forecast of the day-ahead market and the modified reservoir storage due to the ancillary service request of the previous day. Sliding Window Technique has been used for successive self-scheduling to ensure profit for the plant.

Keywords: ancillary services, BPSO, power system economics, self-scheduling, sliding window technique

Procedia PDF Downloads 387

Authors: Eloise C. Tredenick, Troy W. Farrell, W. Alison Forster, Steven T. P. Psaltis

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The agriculture industry requires improved efficacy of sprays being applied to crops. More efficacious sprays provide many environmental and financial benefits. The plant leaf cuticle is known to be the main barrier to diffusion of agrochemicals within the leaf. The importance of a mathematical model to simulate uptake of agrochemicals in plant cuticles has been noted, as the results of each uptake experiments are specific to each formulation of active ingredient and plant species. In this work we develop a mathematical model and numerical simulation for the uptake of ionic agrochemicals through aqueous pores in plant cuticles. We propose a nonlinear porous diffusion model of ionic agrochemicals in isolated cuticles, which provides additions to a simple diffusion model through the incorporation of parameters capable of simulating plant species' variations, evaporation of surface droplet solutions and swelling of the aqueous pores with water. The model could feasibly be adapted to other ionic active ingredients diffusing through other plant species' cuticles. We validate our theoretical results against appropriate experimental data, discuss the key sensitivities in the model and relate theoretical predictions to appropriate physical mechanisms.

Keywords: aqueous pores, ionic active ingredient, mathematical model, plant cuticle, porous diffusion

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Authors: Nurudeen O. Lasisi, Sirajo Abdulrahman, Abdulkareem A. Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with the virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of the modeling of the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. The comparison of Vaccination, linear incident rate and novel quarantine-adjusted incident rate in the models are discussed. The dynamics of the models yield disease-free and endemic equilibrium states.The effective reproduction numbers of the models are computed in order to measure the relative impact of an individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models and we found that the stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, Endemic state, Mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Hamdy Khamis Korayem, Manal Yehia Tayel, Abeer Shawky El Hadedy, Emmanuel Kamal Aziz Saba, Shimaa Badr Abdelnaby Badr

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The aim of the current study was to demonstrate whether serum Interleukin-18 (IL-18) is increased in rheumatoid arthritis (RA) and its correlation with disease activity, functional disability and quality of life in RA patients. The study included 30 RA patients and 20 healthy normal control subjects. The RA patients were diagnosed according to the 2010 ACR/EULAR classification criteria for RA with the exclusion of those who had diabetes mellitus, endocrine disorders, associated rheumatologic diseases, viral hepatitis B or C and other diseases with increased serum IL-18 level. All patients were subjected to clinical evaluation of the musculoskeletal system. Disease activity was assessed by disease activity score 28 with 4 variables (DAS 28). Functional disability was assessed by health assessment questionnaire disability index (HAQ-DI). The quality of life was assessed by Short form-36 (SF-36) questionnaire. Radiological assessment of both hands and feet by Sharp/van der Heijde (SvH) scoring method. Laboratory parameters including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (ACPA) were assessed in patients and serum level of IL-18 in both patients and control subjects. There was no statistically significant difference between patient and control group as regards age and sex. Among patients, 29 % were females and the age range was between 25 to 55 years. Extra-articular manifestations were presented in 56.7% of the patients. The mean of DAS 28 score was 5.73±1.46 and that of HAQ-DI was 1.22±0.72 while that of SF-36 was 40.03±13.96. The level of serum IL-18 was significantly higher in patients than in the control subjects (P= 0.030). Serum IL-18 was correlated with ACPA among the patient group. There were no statistically significant correlations between serum IL-18 and DAS28, HAQ-DI, SF-36, total SvH score and the other laboratory results. In conclusion, IL-18 is significantly higher in RA patient than in healthy control subjects and positively correlated with ACPA level. IL-18 is associated with extra-articular manifestations. However, it is not correlated with other laboratory parameters, disease activity, functional disability, quality of life nor radiological severity.

Keywords: disease activity score, Interleukin-18, quality of life assessment, rheumatoid arthritis

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Authors: A. M. Daneshian Moghaddam, A. H. Hosseinzadeh, A. Bandehagh

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To investigate the effect of cadmium heavy metal stress on five ecotype of dill, this experiment was conducted in the greenhouse of Tabriz University and Shabestar Islamic Azad University’s laboratories with tree replications. After growing the plants, cadmium treatments (concentration 0,300, 600 µmol) were applied. The essential oil of the samples was measured by hydro distillation and using a Clevenger apparatus. Variables used in this study include: wet and dry roots and aerial part of plant, plant height, stem diameter, and root length. The results showed that different concentrations of heavy metal has statistical difference (p < 0.01) on the fresh weight, dry weight, plant height and root length but hadn’t significant difference on essential oil percentage and root length. Dill ecotypes have statistical significant difference on essential oil percent, fresh plant weight, plant height, root length, except plant dry weight. The interactions between Cd concentration and dill ecotypes have not significant effect on all traits, except root length. Maximum fresh weight (4.98 gr) and minimum amount (3.13 gr) were obtained in control trait and 600 ppm of cd concentration, respectively. Highest amount of fresh weight (4.78 gr) was obtained in Birjand ecotype. Maximum plant dry weight (1.2 gr) was obtained at control. The highest plant height (32.54 cm) was obtained in control and with applies cadmium concentrations from zero to 300 and 600 ppm was found significantly reduced in plant height.

Keywords: pollution, essential oil, ecotype, dill, heavy metals, cadmium

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Authors: Abdelouahab Diafat, Meribai Abdelmalek, Ahmed Bahloul

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phytoremediation is the use of plants to remove or degrade organic or inorganic contaminants from soil and water this work aims to study the potential effect of malva sylvestris for the phytoremediation of soils contaminated by Zn. plants were grown in pots containing soil artificially contaminated with Zn at concentrations of 100, 200, and 300 mg/kg. the results obtained show that the Zn concentrations used have a negative effect on the growth of this plant the search for the metal carried out by the technique of atomic absorption spectrometry shows that this plant accumulates a small quantity of this metal. it can be concluded that the malva sylvestris plant tolerates Zn contaminated soils but it is not considered as a zinc hyperaccumulator plant

Keywords: phytoremidiation, Zn-contaminated soils, Malva Sylvestris, phytoextraction

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Atif Naim, Faisal E. Ahmed, Sershen

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A field experiment was conducted for two consecutive winter seasons at the Demonstration Farm of the Faculty of Agriculture, University of Khartoum, Sudan, to study effects of different levels of irrigation regime and plant density on yield of introduced small seeded (desi type) chickpea cultivar (ILC 482). The experiment was laid out in a 3X3 factorial split-plot design with 4 replications. The treatments consisted of three irrigation regimes (designated as follows: I1 = optimum irrigation, I2 = moderate stress and I3 = severe stress; this corresponded with irrigation after drainage of 50%, 75% and 100% of available water based on 70%, 60% and 50% of field capacity, respectively) assigned as main plots and three plant densities (D₁=20, D₂= 40 and D₃= 60 plants/m²) assigned as subplots. The results indicated that the yield components (number of pods per plant, number of seeds per pod, 100 seed weight), seed yield per plant, harvest index and yield per unit area of chickpea were significantly (p < 0.05) affected by irrigation regime. Decreasing irrigation regime significantly (p < 0.05) decreased all measured parameters. Alternatively, increasing plant density significantly (p < 0.05) decreased the number of pods and seed yield per plant and increased seed yield per unit area. While number of seeds per pod and harvest index were not significantly (p > 0.05) affected by plant density. Interaction between irrigation regime and plant density was also significantly (p < 0.05) affected all measured parameters of yield, except for harvest index. It could be concluded that the best irrigation regime was full irrigation (after drainage of 50% available water at 70% field capacity) and the optimal plant density was 20 plants/m² under conditions of semi-arid regions.

Keywords: irrigation regime, Cicer arietinum, chickpea, plant density

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Authors: Nasar Aldian Ambark Shashoa

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Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.

Keywords: analytical redundancy, fault detection, feature extraction, Bayesian approach

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Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Sansanee Sansiribhan, Anusorn Rattanathanaophat, Chirapan Nuengchaknin

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The potential feasibility of a 9.5 MWe capacity rice straw power plant project in Thailand was studied by evaluating the rice straw resource. The result showed that Thailand had a high rice straw biomass potential at the provincial level, especially, the provinces in the central, northeastern and western Thailand, which could feasibly develop plants. The economic feasibility of project was also investigated. The financial feasibility is also evaluated based on two important factors in the project, i.e., NPV ≥ 0 and IRR ≥ 11%. It was found that the rice straw power plant project at 9.5 MWe was financially feasible with the cost of fuel in the range of 30.6-47.7 USD/t.

Keywords: power plant, project feasibility, rice straw, Thailand

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Authors: Mathias B. Michael, Esther T. Akinlabi, Tien-Chien Jen

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Thermal energy consumption in palm oil production plant comprises mainly of steam, hot water and hot air. In most efficient plants, hot water and air are generated from the steam supply system. Research has shown that thermal energy utilize in palm oil production plants is about 70 percent of the total energy consumption of the plant. In order to manage the plants’ energy efficiently, the energy systems are modelled and optimized. This paper aimed to present the model of steam supply systems of a typical palm oil production plant in Ghana. The models include exergy and energy models of steam boiler, steam turbine and the palm oil mill. The paper further simulates the virtual plant model to obtain the thermal energy performance of the plant under study. The simulation results show that, under normal operating condition, the boiler energy performance is considerably below the expected level as a result of several factors including intermittent biomass fuel supply, significant moisture content of the biomass fuel and significant heat losses. The total thermal energy performance of the virtual plant is set as a baseline. The study finally recommends number of energy efficiency measures to improve the plant’s energy performance.

Keywords: palm biomass, steam supply, exergy and energy models, energy performance benchmark

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Authors: Afsoon Danesh Afrooz, Sohrab Imani, Ali Ahadiyat, Aref Maroof, Yahya Ostadi

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This study has been investigated for finding alternative and safe botanical pesticides instead of chemical insecticides. The effects of plant extracts of Eucalyptus camaldulensis and Thymus daenensis were tested against adult of Callosobrochus maculatus F. Experiments were carried out at 27±1°C and 60 ± 5% R. H. under dark condition with adopting a complete randomized block design. Three replicates were set up for five concentrations of each plants extract. LC50 values were determined by SPSS 16.0 software. LC50 values indicated that plant extract of Thymus daenensis with 1.708 (µl/l air) against adult was more effective than the plant extract of Eucalyptus camaldulensis with LC50 12.755 (µl/l air). It was found that plant extract of Thymus daenensis in comparison with extract of Eucalyptus camaldulensis could be used as a pesticide for control store pests.

Keywords: callosobruchus maculatus, Eucalyptus camaldulensis, insecticidal effects, Thymus daenensis

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Authors: Emel Yildiz, Nurcan Biçer, Fazilet Aksu, Arash Alizadeh Yegani

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Plants provide the wealth of bioactive compounds, which exert a substantial strategy for the treatment of neurological disorders such as Alzheimer's disease. Recently, a lot of studies have explored the medicinal properties of curcumin, including antitumoral, antimicrobial, anti-inflammatory, antioxidant, antiviral, and anti-Alzheimer's disease effects. Metal complexes of curcumin (1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione) were synthesized with Mn(II) and Fe(III). The structures of synthesized metal complexes have been characterized by using spectroscopic and analytic methods such as elemental analysis, magnetic susceptibility, FT-IR, AAS, TG and argentometric titration. It was determined that the complexes have octahedral geometry. The effects of the metal complexes on the disorder of memory, which is an important symptom of Alzheimer's Disease were studied on lab rats with Plus-Maze Tests at Behavioral Pharmacology Laboratory.

Keywords: curcumin, Mn(II), Fe(III), Alzheimer disease, beta amyloid 25-35

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Authors: Aftab Ahmed, Ghulam Mehboob

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The object of presentation is to highlight the importance of condition which is a very rare genetic disorder although Paget’s disease is common but its juvenile type is very rare and a late presentation due to very slow onset and lack of earlier standard management. We present a case of 25 years old male with a chronic history of bone pain and a slow onset of mild swelling, later on diagnosed as juvenile Paget disease of bone. Rarity of this condition with inaccessibility for standard health treatment can lead to a significant delay in presentation and its management. There have been 50 reported cases worldwide according to Genetic Home Reference. There is increased osteoclastic activity along with osteoblastic activity related to gene alteration and osteoprotegrin deficiency. Morbidity of disease is very significant which lead children to become immobilize.

Keywords: juvenile, Paget’s disease, bone, Northern Area of Pakistan

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Authors: Vikas Baliram Kalyankar

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Study of ecology is important as it plays role in development of environment engineering. With the advent of technologies the study of ecosystem structure and changes in it are remaining unnoticed. The ecological succession is the sequential replacement of plant species following changes in the environment. The present study depicts the primary ecological succession in an area leveled up to the height of five feet with no signs of plant life on it. The five quadrates of 1 meter square size were observed during the study period of six months. Rain water being the only source of water in the area increased its ecological importance. The primary successor was non- leguminous plant Balonites roxburgii during the peak drought periods in the region of the summer 2013-14. The increased nitrogen content of soil after the plant implied its role in atmospheric nitrogen fixation.

Keywords: succession, Balonites roxburgii, non-leguminous plant, ecology

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Authors: Temitayo Abayomi Ewemoje, Isaac Olugbemiga Afolayan, Badmus Alao Tayo

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Researchers in developing countries have worked on improving cassava resistance to diseases and pests, high yielding and early maturity However, water management has received little or no attention as cassava cultivation in Sub-Saharan Africa depended on available precipitation (rain-fed condition). Therefore the need for water management in Agricultural crop production cannot be overemphasized. As other sectors compete with agricultural sector for fresh water (which is not readily available), there is need to increase water productivity in agricultural production. Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had highest number of nodes. Tuber stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions.Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had the highest number of nodes. Tuber, stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions

Keywords: improved TMS varieties, leaf productivity, rain-fed cassava production, stem productivity, tuber productivity

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Authors: H. Chabalala, A. Grootboom, M. Tang

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The innovative development, production, and clinical utilisation of African natural medicines requires frameworks from systematisation, innovation, registration. Afrika faces challenges when it comes to these sectors. The opposite is the case as is is evident in ancient Asian (Traditional Chinese Medicine and Indian Ayurveda and Siddha) medical systems, which are interfaced into their respective national health and educational systems. Afrikan Natural Medicines (ANMs) are yet to develop systematisation frameworks, i.e. disease characterisation and medicines classification. This paper explores classical medical systems drawn from Afrikan and Chinese experts in natural medicines. An Afrikological research methodology was used to conduct in-depth interviews with 20 key respondents selected through purposeful sampling technique. Data was summarised into systematisation frameworks for classical disease theories, patient categorisation, medicine classification, aetiology and pathogenesis of disease, diagnosis and prognosis techniques and treatment methods. It was discovered that ancient Afrika had systematic medical cosmologies, remnants of which are evident in most Afrikan cultural health practices. Parallels could be drawn from classical medical concepts of antiquity, like Chinese Taoist and Indian tantric health systems. Data revealed that both the ancient and contemporary ANM systems were based on living medical cosmologies. The study showed that African Natural Healing Systems have etiological systems, general pathogenesis knowledge, differential diagnostic techniques, comprehensive prognosis and holistic treatment regimes. Systematisation models were developed out of these frameworks, and this could be used for evaluation of clinical research, medical application including development of curriculum for high-education. It was envisaged that frameworks will pave way towards the development, production and commercialisation of ANMs. This was piloted in inclusive innovation, technology transfer and commercialisation of South African natural medicines, cosmeceuticals, nutraceuticals and health infusions. The central model presented here in will assist in curriculum development and establishment of Afrikan Medicines Hospitals and Pharmaceutical Industries.

Keywords: African Natural Medicines, Indigenous Knowledge Systems, Medical Cosmology, Clinical Application

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Authors: Olofintoye Lawrence Kayode, Olorunniyi Omojola Felix

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Schistosomiasis is a vector-borne water-based disease transmitted by Bulinus globosus, causing haematuria in the urine of man, while fascioliasis is a trematode zoonosis infectious transmitted by Lymnaea natalensis causing liver disease in man and animals. Adult Bulinus globosus and Lymnaea natalensis were used for the experiment. Aqueous leaf extract of Ageratum conyzoides and Datura stramonium were prepared into 25, 50, 75, 100, 200 and 400 ppm concentrations. Ten snails of each species were exposed to different concentrations in triplicates, and dechlorinated water was used as control at 24h, 48h, and 72h exposure. The results revealed that 100 ppm of both plants leaves extracts indicated mortality rates between 76.7% and 100% at 24h, 48h, and 72h for both snail species. (P<0.05). In conclusion, the extract exercised molluscicidal activity to control the snail vector at lethal doses LC₅₀ (66.611- 72.021 ppm), CI = 63.083-77.90ppm and LC₉₀ (92.623-102.350), CI = 87.715 -110.12 ppm.

Keywords: snail, plant leaf, aqueous extract, mortality

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Authors: Hasan Dalgic, Fikret Akinerdem

Abstract:

This trial was made to determine effect of different plant density and weed control on yield and quality of winter sowing safflower (Carthamus tinctorius L.) in Selcuk University, Agricultural Faculty trial fields and the effective substance of Trifluran was used as herbicide. Field trial was made during the vegetation period of 2009-2010 with three replications according to 'Split Plots in Randomized Blocks' design. The weed control techniques were made on main plots and row distances was set up on sub-plots. The trial subjects were consisting from three weed control techniques as fallowing: herbicide application (Trifluran), hoeing and control beside the row distances of 15 cm and 30 cm. The results were ranged between 59.0-76.73 cm in plant height, 40.00-47.07 cm in first branch height, 5.00-7.20 in number of branch per plant, 6.00-14.73 number of head per plant, 19.57-21.87 mm in head diameter, 2125.0-3968.3 kg ha-1 in seed yield, 27.10-28.08 % in crude oil rate and 531.7-1070.3 kg ha-1. According to the results, Remzibey safflower cultivar showed the highest seed yield on 30 cm of row distance and herbicide application by means of the direct effects of plant height, first branch height, number of branch per plant, number of head per plant, table diameter, crude oil rate and crude oil yield.

Keywords: safflower, herbicide, row spacing, seed yield, oil ratio, oil yield

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Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

Abstract:

In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

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Authors: Salam N. Abdo, Orien L. Tulp, George P. Einstein

Abstract:

The purpose of this exploratory study was to gather the insights into psoriasis etiology, treatment, and patient experience, for developing psoriasis and psoriatic arthritis diagnostic test. Data collection methods consisted of a comprehensive meta-analysis of relevant studies and psoriasis patient survey. Established meta-analysis guidelines were used for the selection and qualitative comparative analysis of psoriasis and psoriatic arthritis research studies. Only studies that clearly discussed psoriasis etiology, treatment, and patient experience were reviewed and analyzed, to establish a qualitative data base for the study. Using the insights gained from meta-analysis, an existing psoriasis patient survey was modified and administered to collect additional data as well as triangulate the results. The hypothesis is that specific types of psoriatic disease have specific etiology and pathophysiologic pattern. The following etiology categories were identified: bacterial, environmental/microbial, genetic, immune, infectious, trauma/stress, and viral. Additional results, obtained from meta-analysis and confirmed by patient survey, were the common age of onset (early to mid-20s) and type of psoriasis (plaque; mild; symmetrical; scalp, chest, and extremities, specifically elbows and knees). Almost 70% of patients reported no prescription drug use due to severe side effects and prohibitive cost. These results will guide the development of psoriasis and psoriatic arthritis diagnostic test. The significant number of medical publications classified psoriatic arthritis disease as inflammatory of an unknown etiology. Thus numerous meta-analyses struggle to report any meaningful conclusions since no definitive results have been reported to date. Therefore, return to the basics is an essential step to any future meaningful results. To date, medical literature supports the fact that psoriatic disease in its current classification could be misidentifying subcategories, which in turn hinders the success of studies conducted to date. Moreover, there has been an enormous commercial support to pursue various immune-modulation therapies, thus following a narrow hypothesis/mechanism of action that is yet to yield resolution of disease state. Recurrence and complications may be considered unacceptable in a significant number of these studies. The aim of the ongoing study is to focus on a narrow subgroup of patient population, as identified by this exploratory study via meta-analysis and patient survey, and conduct an exhaustive work up, aiming at mechanism of action and causality before proposing a cure or therapeutic modality. Remission in psoriasis has been achieved and documented in medical literature, such as immune-modulation, phototherapy, various over-the-counter agents, including salts and tar. However, there is no psoriasis and psoriatic arthritis diagnostic test to date, to guide the diagnosis and treatment of this debilitating and, thus far, incurable disease. Because psoriasis affects approximately 2% of population, the results of this study may affect the treatment and improve the quality of life of a significant number of psoriasis patients, potentially millions of patients in the United States alone and many more millions worldwide.

Keywords: biologics, early diagnosis, etiology, immune disease, immune modulation therapy, inflammation skin disorder, phototherapy, plaque psoriasis, psoriasis, psoriasis classification, psoriasis disease marker, psoriasis diagnostic test, psoriasis marker, psoriasis mechanism of action, psoriasis treatment, psoriatic arthritis, psoriatic disease, psoriatic disease marker, psoriatic patient experience, psoriatic patient quality of life, remission, salt therapy, targeted immune therapy

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